A Visualized Bibliometric Analysis of Artificial Intelligence based on Biblioshiny (2014-2023)

Abstract

This paper is based on the artificial intelligence literature in the Web of Science™ Core Collection database from 2014 to 2023. Bibliometric methods are used to analyze the number of publications, highly productive authors, highly cited literature, research hotspots, and trends in the field with the help of the Biblioshiny program in R language. The hotspots of artificial intelligence research include data mining, prediction, classification, intelligent algorithms, deep learning and so on. In the future, AI will focus on the development of natural language processing technology and deep learning under the trend of interdisciplinary diversification, focusing on the analysis of Explainable Artificial Intelligence (XAI). At the same time, we will optimize algorithms and use multiple research methods to explore different hot topics in depth.

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